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Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness ofsmall electronic devices that allow interaction with them via speech. Often,KWS systems are speaker-independent, which means that any person --user ornot-- might trigger them. For applications like KWS for hearing assistivedevices this is unacceptable, as only the user must be allowed to handle them.In this paper we propose KWS for hearing assistive devices that is robust toexternal speakers. A state-of-the-art deep residual network for small-footprintKWS is regarded as a basis to build upon. By following a multi-task learningscheme, this system is extended to jointly perform KWS and users'own-voice/external speaker detection with a negligible increase in the numberof parameters. For experiments, we generate from the Google Speech CommandsDataset a speech corpus emulating hearing aids as a capturing device. Ourresults show that this multi-task deep residual network is able to achieve aKWS accuracy relative improvement of around 32% with respect to a system thatdoes not deal with external speakers.

Keyword Spotting for Hearing Assistive Devices Robust to External Speakers